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indexing.py

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+"""
+Indexing with vector database
+"""
+
+from pathlib import Path
+import re
+
+import chromadb
+
+from unidecode import unidecode
+
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_chroma import Chroma
+from langchain_huggingface import HuggingFaceEmbeddings
+
+
+
+# Load PDF document and create doc splits
+def load_doc(list_file_path, chunk_size, chunk_overlap):
+    """Load PDF document and create doc splits"""
+
+    loaders = [PyPDFLoader(x) for x in list_file_path]
+    pages = []
+    for loader in loaders:
+        pages.extend(loader.load())
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size, chunk_overlap=chunk_overlap
+    )
+    doc_splits = text_splitter.split_documents(pages)
+    return doc_splits
+
+
+# Generate collection name for vector database
+#  - Use filepath as input, ensuring unicode text
+#  - Handle multiple languages (arabic, chinese)
+def create_collection_name(filepath):
+    """Create collection name for vector database"""
+
+    # Extract filename without extension
+    collection_name = Path(filepath).stem
+    # Fix potential issues from naming convention
+    ## Remove space
+    collection_name = collection_name.replace(" ", "-")
+    ## ASCII transliterations of Unicode text
+    collection_name = unidecode(collection_name)
+    ## Remove special characters
+    collection_name = re.sub("[^A-Za-z0-9]+", "-", collection_name)
+    ## Limit length to 50 characters
+    collection_name = collection_name[:50]
+    ## Minimum length of 3 characters
+    if len(collection_name) < 3:
+        collection_name = collection_name + "xyz"
+    ## Enforce start and end as alphanumeric character
+    if not collection_name[0].isalnum():
+        collection_name = "A" + collection_name[1:]
+    if not collection_name[-1].isalnum():
+        collection_name = collection_name[:-1] + "Z"
+    print("\n\nFilepath: ", filepath)
+    print("Collection name: ", collection_name)
+    return collection_name
+
+
+# Create vector database
+def create_db(splits, collection_name):
+    """Create embeddings and vector database"""
+
+    embedding = HuggingFaceEmbeddings(
+        model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
+        # model_name="sentence-transformers/all-MiniLM-L6-v2",
+        # model_kwargs={"device": "cpu"},
+        # encode_kwargs={'normalize_embeddings': False}
+    )
+    chromadb.api.client.SharedSystemClient.clear_system_cache()
+    new_client = chromadb.EphemeralClient()
+    vectordb = Chroma.from_documents(
+        documents=splits,
+        embedding=embedding,
+        client=new_client,
+        collection_name=collection_name,
+        # persist_directory=default_persist_directory
+    )
+    return vectordb

prompt_template.json

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+{
+	"title": "System prompt",
+	"prompt": "You are an assistant for question-answering tasks. Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Keep the answer concise. Question: {question}  \\n Context: {context} \\n Helpful Answer:"
+}
+